Abstract：
Objective. Multi-channel electroencephalogram (EEG) technology in brain-computer interface (BCI) research offers the advantage of enhanced spatial resolution and system performance. However, this also implies that more time is needed in the data processing stage, which is not conducive to the rapid response of BCI. Hence, it is a necessary and challenging task to reduce the number of EEG channels while maintaining decoding effectiveness.Approach. In this paper, we propose a local optimization method based on the Fisher score for within-subject EEG channel selection. Initially, we extract the common spatial pattern characteristics of EEG signals in different bands, calculate Fisher scores for each channel based on these characteristics, and rank them accordingly. Subsequently, we employ a local optimization method to finalize the channel selection.Main results. On the BCI Competition IV Dataset IIa, our method selects an average of 11 channels across four bands, achieving an average accuracy of 79.37%. This represents a 6.52% improvement compared to using the full set of 22 channels. On our self-collected dataset, our method similarly achieves a significant improvement of 24.20% with less than half of the channels, resulting in an average accuracy of 76.95%.Significance. This research explores the importance of channel combinations in channel selection tasks and reveals that appropriately combining channels can further enhance the quality of channel selection. The results indicate that the model selected a small number of channels with higher accuracy in two-class motor imagery EEG classification tasks. Additionally, it improves the portability of BCI systems through channel selection and combinations, offering the potential for the development of portable BCI systems.
摘要：
Objective.脑机接口（BCI）研究中的多通道脑电图（EEG）技术提供了增强的空间分辨率和系统性能的优势。然而,这也意味着在数据处理阶段需要更多的时间，不利于BCI的快速反应。因此，在保持解码有效性的同时减少EEG通道的数量是一项必要且具有挑战性的任务。方法。在本文中,我们提出了一种基于Fisher评分的受试者内脑电通道选择的局部优化方法。最初，提取不同波段脑电信号的共同空间模式特征,根据这些特征计算每个通道的Fisher分数，并对它们进行相应的排名。随后，我们采用局部优化方法来完成信道选择。主要结果。关于BCI竞赛IV数据集IIa，我们的方法在四个波段平均选择11个通道，平均准确率为79.37%。这表示与使用22个通道的全组相比6.52%的改进。在我们自己收集的数据集上，我们的方法同样用不到一半的通道实现了24.20%的显著改进，平均准确率为76.95%。意义。这项研究探讨了信道组合在信道选择任务中的重要性，并揭示了适当的信道组合可以进一步提高信道选择的质量。结果表明，该模型在两类运动想象脑电分类任务中选择了少量具有较高准确性的通道。此外,它通过信道选择和组合提高了BCI系统的可移植性，为便携式BCI系统的开发提供了潜力。
